The widespread and diverse online media platforms and other internet-driven communication technologies have presented significant challenges in defining the boundaries of freedom of expression. Consequently, the internet has been transformed into a potential cyber weapon. Within this evolving landscape, two particularly hazardous phenomena have emerged: fake news and doxxing. Although these threats have been subjects of extensive scholarly analysis, the crossroads where they intersect remain unexplored. This research addresses this convergence by introducing a novel system. The Fake News and Doxxing Detection with Explainable Artificial Intelligence (FNDEX) system leverages the capabilities of three distinct transformer models to achieve high-performance detection for both fake news and doxxing. To enhance data security, a rigorous three-step anonymization process is employed, rooted in a pattern-based approach for anonymizing personally identifiable information. Finally, this research emphasizes the importance of generating coherent explanations for the outcomes produced by both detection models. Our experiments on realistic datasets demonstrate that our system significantly outperforms the existing baselines

翻译：在线媒体平台及其他互联网驱动的通信技术的广泛普及与多样化，对界定言论自由的边界提出了重大挑战。因此，互联网已演变为一种潜在的网络武器。在这一不断发展的环境中，出现了两种尤其危险的现象：虚假新闻与人肉搜索。尽管这些威胁已成为广泛学术分析的主题，但其交叉领域仍未得到探索。本研究通过引入一种新颖系统来应对这一交汇点。基于可解释人工智能的虚假新闻与人肉搜索检测系统（FNDEX）利用三种不同的Transformer模型的能力，实现了对虚假新闻和人肉搜索的高性能检测。为增强数据安全性，系统采用了一个严格的三步匿名化流程，其基础是一种基于模式的个人可识别信息匿名化方法。最后，本研究强调了为两种检测模型所产生的结果生成连贯解释的重要性。我们在真实数据集上的实验表明，我们的系统显著优于现有基线方法。